Swarm Learning IRS in 6G-Metaverse: Secure Configurable Resources Trading for Reliable XR Communications

The emerging Metaverse has challenging requirements for the reliability of extended reality (XR) data transmission. Configurable communication is a promising technology to improve the XR communication performance, where the intelligent reflecting surface (IRS) is representative of the ability to control transmission channels. However, because of the absence of incentives and untrust among IRS and Metaverse users, there is no easy way to establish the configuration resource scheduling for XR communication. Existing trusted third party-based methods face single-point/collusion attacks, inefficiency in arbitration, and low intelligence problems. To solve these problems, we propose a swarm learning (SL)-based secure configurable resource trading mechanism for reliable 6G-Metaverse XR communication. First, an SL-based configurable resource trading framework is established, which includes two designed subchains for decentralized IRS resource management and intelligent allocation. Second, a smart contract-enabled configurable resource trading scheme is designed, where decentralized trust is built among IRS devices, Metaverse users, and base stations. Third, we propose a decentralized federated learning (FL)-driven IRS allocation scheme, which consists of XR communication-related data collection, model training, and resource configuration. Finally, experimental results demonstrate the effectiveness of the proposed SL-based configurable resource trading for reliable XR communication.